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11 Examples to Master Python List Comprehensions


Tip: When you are not sure and cannot find out the syntax for list comprehension, try to build it with for/if loops. Then you can convert it to a list comprehension by adding individual blocks of loops into the list comprehension. This is acceptable or even desirable for small or medium-sized lists because it makes the operation faster. However, when we are working with large lists (e.g. 1 billion elements), list comprehension should be avoided. It may cause your computer to crash due to the extreme amount of memory requirement. A better alternative for such large lists is using a generator that does not actually create a large data structure in memory.

Understanding nested list comprehension syntax in Python -- /var/


List comprehensions are one of the really nice and powerful features of Python. It is actually a smart way to introduce new users to functional programming concepts (after all a list comprehension is just a combination of map and filter) and compact statements. However, one thing that always troubled me when using list comprehensions is their non intuitive syntax when nesting was needed. For example, let's say that we just want to flatten a list of lists using a nested list comprehension: At this time I'd need research google for a working list comprehension syntax and adjust it to my needs (or give up and write it as a double for loop). What if I wanted to add a third level of nesting or an if? Well I'd just bite the bullet and use for loops!

How to Convert Loops to List Comprehension in Python


List comprehension is used for creating lists based on iterables. It can also be described as representing for and if loops with a simpler and more appealing syntax. List comprehensions are relatively faster than for loops. The syntax of a list comprehension is actually easy to understand. However, when it comes to complex and nested operations, it might get a little tricky to figure out how to structure a list comprehension.

Introduction to Python Loops


Instruction, media content, examples and links to resources that will help you build a foundation for Python competency. In the real world, you often need to repeat something over and over. When programming, though, if you need to do something 100 times, you certainly don't need to write it out in 100 identical lines of code. In Python, loops allow you to iterate over a sequence, whether that's a list, tuple, string, or dictionary. There is a for loop and a while loop.

Python Data Science Toolbox (Part 2)


In this second course in the Python Data Science Toolbox, you'll continue to build your Python Data Science skills. First you'll enter the wonderful world of iterators, objects that you have already encountered in the context of for loops without having necessarily known it. You'll then learn about list comprehensions, which are extremely handy tools that form a basic component in the toolbox of all modern Data Scientists working in Python. You'll end the course by working through a case study in which you'll apply all of the techniques you learned both in this course as well as the prequel. If you're looking to make it as a Pythonista Data Science ninja, you have come to the right place.